Placement Student - Service Engineering - Digital and Data Analytics Review

During my placement, I was heavily involved in hands-on analytics, automation, and machine learning-related work, including:
Developing failure mode prediction models using machine learning
Exploring image recognition for engineering inspection use cases
Performing data analysis and building Power BI reporting solutions
For example, I:
Analysed data completeness and field usage within datasets to improve data quality and reporting accuracy
2) CCIMS 2.0 (major focus)
A significant part of my role focused on the development of CCIMS 2.0, the next-generation failure analysis system (Data repository and entry system, which service engineering use to store and records data from failed parts), including expanding the scope of the software to the electrification side of the business.
My responsibilities included:
Gathering user feedback globally through Voice of Customer (VOC) sessions
Supporting system design decisions and proposing improvements
Reviewing timelines and helping improve planning clarity
Exploring data structures, ingestion processes, and reporting requirements
For example, I:
Organised and led a global VOC session to collect feedback from international stakeholders
Provided feedback on project timelines to improve visibility for end users
3) Data engineering & automation
I worked on building real, production-relevant workflows rather than purely analytical tasks.
This included:
Developing automated data pipelines using Databricks (Python)
Generating warranty report outputs in required Excel formats
Automating data delivery to SharePoint/OneDrive locations
Working through enterprise governance processes (e.g. app registrations and permissions)
For example, I:
Built a scheduled pipeline to extract claims data and recreate warranty reports used by the business
Worked directly with CloudOps and IAM teams to obtain approvals and enable automation
4) Stakeholder collaboration & requirements gathering
I regularly collaborated with a range of stakeholders, including:
Service engineers
Warranty teams
IT / CloudOps teams
Global teams (e.g. Brazil and the US)
My day-to-day activities included:
Gathering requirements and feedback from users
Clarifying technical feasibility
Aligning across multiple teams and functions
For example, I:
Initiated discussions with global engineers on AI-based inspection reporting solutions
Coordinated with stakeholders to validate data and prepare demos for warranty use cases
5) Tooling, demos, and proof-of-concepts
I contributed to the development and evaluation of tools and proof-of-concepts by:
Building demo datasets and tools for stakeholder validation
Exploring system limitations (e.g. APIs, data export capabilities)
Assessing feasibility of potential solutions
For example, I:
Investigated options for exporting attachments using APIs and other tools
Built demo outputs to demonstrate how solutions would work for warranty teams.
6) Broader contributions
In addition to technical work, I contributed to wider team activities:
Owned and produced department newsletter
Supported internal events such as Insight days
Typical Day-to-Day Activities
On a day-to-day basis, my role involved:
Writing Python/Databricks code to develop data pipelines
Analysing warranty and failure data
Attending and organising stakeholder meetings
Gathering and refining business requirements
Communicating with IT teams to unblock technical work
Preparing demos and validating outputs with users
Reviewing documentation and improving clarity
Iterating on solutions based on feedback

I was trusted with a level of responsibility comparable to an early-career engineer, including:
Presenting my work to a director level
Influencing design and process decisions
Working both independently with global and regional teams

Analysed complex engineering and warranty datasets to generate actionable insights
Developed Power BI dashboards to support data-driven decision-making
Built automated data pipelines using Python and Databricks
Worked with large-scale data processing in Spark-based environments
Applied machine learning techniques to predict engine failure modes
Explored AI/ML use cases for engineering inspection and reporting workflows
Assessed data quality and completeness, identifying improvements to data accuracy
Translated business requirements into technical data and reporting solutions
Supported development of an enterprise system (CCIMS 2.0) through design input and feedback
Evaluated data structures, ingestion processes, and reporting outputs for system improvements
Collaborated with cross-functional stakeholders including engineering, IT, and global teams
Led and facilitated meetings to gather user requirements and feedback
Communicated technical concepts clearly to both technical and non-technical audiences
Navigated enterprise IT governance processes (Azure, permissions, app registrations)
Demonstrated ownership by delivering end-to-end solutions from design through to deployment
Solved real-world problems by adapting to incomplete data and system constraints
Managed multiple tasks independently while maintaining attention to detail

Lots of independent work, although managers very willing to help when asked.

Very welcoming for everyone, flexible for your schedule.

Thoroughly enjoyed it. I felt satisfied that I have had great personal development during my time here.

Yes